Senior ML Platform & MLOps Engineer — Production-Scale ML

Zoolatech; Fatih; Presencial; TRY 450.000 - 900.000; Jornada completa

Descripción de la vacante

Zoolatech is building a shared ML platform to empower product teams to develop, deploy, and scale ML solutions across Europe. This platform-level role blends hands-on engineering with architectural input to define how ML infrastructure operates at scale and supports early production rollout.

You will work with Applied Scientists, ML Engineers, and Platform teams to evaluate capabilities, ensure reliability, and onboard scaling teams while shaping governance standards and best practices.

Formación

• 5+ years building and operating production-grade ML or large-scale data systems on cloud platforms.
• Experience with distributed systems (Docker and Kubernetes).
• Experience with streaming or batch processing systems (e.g. Kafka, Spark, Flink).
• Experience with ML lifecycle orchestration and workflow systems (e.g. Metaflow / Outerbounds, Airflow, Kubeflow Pipelines, Dagster, Flyte, Prefect).
• Experience designing reproducible ML pipelines and supporting model training, evaluation, and deployment workflows across teams.
• Strong understanding of reliability, monitoring, and safe deployment practices.

Responsabilidades

• Evaluate platform capabilities (GPU training, real-time and batch inference, orchestration, scalability).
• Analyze performance, reliability, and integration with existing systems.
• Contribute to key technical decisions (build vs buy, tooling, target stack).
• Support early production rollout and integration into the broader ecosystem.
• Enable adoption of the platform by initial and scaling teams.

Conocimientos

Distributed systems

MLOps

Platform experience

Cross-team collaboration

Herramientas

Docker

Kubernetes

Kafka

Spark

Flink

Metaflow

Airflow

Kubeflow Pipelines

Dagster

Flyte

Prefect

Databricks

Descripción del empleo

Zoolatech is building a shared ML platform to empower product teams to develop, deploy, and scale ML solutions across Europe. This platform-level role blends hands-on engineering with architectural input to define how ML infrastructure operates at scale and supports early production rollout.

You will work with Applied Scientists, ML Engineers, and Platform teams to evaluate capabilities, ensure reliability, and onboard scaling teams while shaping governance standards and best practices.